A Novel Design Methodology for a Multioctave GaN-HEMT Power Amplifier Using Clustering Guided Bayesian Optimization

Abstract

The increasing demand of high-performance power amplifiers (PAs) for modern wireless systems has led the PA structures becoming more complex, thereby resulting in an extremely difficult optimization process for PA design. In this paper, the Bayesian optimization (BO) algorithm with a novel acquisition function, namely clustering guided Gaussian process upper confidence bound (CG-GPUCB) method, is proposed for the optimization of a multi-octave PA. To validate the developed optimization strategy, a high-performance multi-octave PA with a 10-W gallium nitride (GaN) transistor has been successfully implemented. The measured performance of the fabricated PA over the frequency band between 0.6 GHz and 2.8 GHz show that the output power is greater than 40 dBm, the power added efficiency (PAE) is over 62%, and the gain is more than 10 dB. Compared with existing BO based method, the proposed methodology is more efficient, since this method can allow achieving better performance for PAs with less optimization time. A comparison between the achieved results and the performance of other state-of-the-art PAs based on different optimization algorithms has highlighted the validity of the proposed design methodology and the obtained improvement in terms of bandwidth.